Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Definitive Guide to Google Vertex AI

You're reading from   The Definitive Guide to Google Vertex AI Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices

Arrow left icon
Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781801815260
Length 422 pages
Edition 1st Edition
Tools
Arrow right icon
Authors (2):
Arrow left icon
Kartik Chaudhary Kartik Chaudhary
Author Profile Icon Kartik Chaudhary
Kartik Chaudhary
Jasmeet Bhatia Jasmeet Bhatia
Author Profile Icon Jasmeet Bhatia
Jasmeet Bhatia
Arrow right icon
View More author details
Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1:The Importance of MLOps in a Real-World ML Deployment
2. Chapter 1: Machine Learning Project Life Cycle and Challenges FREE CHAPTER 3. Chapter 2: What Is MLOps, and Why Is It So Important for Every ML Team? 4. Part 2: Machine Learning Tools for Custom Models on Google Cloud
5. Chapter 3: It’s All About Data – Options to Store and Transform ML Datasets 6. Chapter 4: Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs 7. Chapter 5: No-Code Options for Building ML Models 8. Chapter 6: Low-Code Options for Building ML Models 9. Chapter 7: Training Fully Custom ML Models with Vertex AI 10. Chapter 8: ML Model Explainability 11. Chapter 9: Model Optimizations – Hyperparameter Tuning and NAS 12. Chapter 10: Vertex AI Deployment and Automation Tools – Orchestration through Managed Kubeflow Pipelines 13. Chapter 11: MLOps Governance with Vertex AI 14. Part 3: Prebuilt/Turnkey ML Solutions Available in GCP
15. Chapter 12: Vertex AI – Generative AI Tools 16. Chapter 13: Document AI – An End-to-End Solution for Processing Documents 17. Chapter 14: ML APIs for Vision, NLP, and Speech 18. Part 4: Building Real-World ML Solutions with Google Cloud
19. Chapter 15: Recommender Systems – Predict What Movies a User Would Like to Watch 20. Chapter 16: Vision-Based Defect Detection System – Machines Can See Now! 21. Chapter 17: Natural Language Models – Detecting Fake News Articles! 22. Index 23. Other Books You May Enjoy

Packaging a model to submit it to Vertex AI as a training job

The previous section demonstrated a small image colorization experiment on a Vertex AI Workbench notebook. Notebooks are great for small-scale and quick experiments, but when it comes to large-scale experiments (with more compute and/or memory requirements), it is advised to launch them as a Vertex AI job and specify desired machine specifications (accelerators such as GPU or TPU if needed) for optimal experimentation. Vertex AI jobs also let us execute tons of experiments in parallel without waiting for the results of a single experiment. Experiment tracking is also quite easy with Vertex AI jobs, so it becomes easier to compare your latest experiments with past experiments with the help of saved metadata and the Vertex AI UI. Now, let’s use our model experimentation setup from the previous section and launch it as a Vertex AI training job.

Important note

Vertex AI jobs run in a containerized environment,...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime